WORLD INEQUALITY DATASET ANALYSIS

Binita Maity , IIT Gandhinagar, binitamaity@iitgn.ac.in

Sai Narasimha , IIT Gandhinagar, narasimha.gs@iitgn.ac.in

G V Sai kumar , IIT Gandhinagar, saikrgavini@iitgn.ac.in

Inroduction

For decades, inequality has been increasing all over the world. Within a single country, inequality can occur. Some people are blessed with resources and others are far away from the light of evolution. However, economic disparities have widened as the world's wealthiest individuals accumulate new levels of wealth.

The types of inequities we have noticed in society are as follows:

Wealth inequality
Treatment and responsibility inequality
Gender economic Inequality

The World Inequality Datasets include factors such as nation data, year value, population, citizen names, and regions. National income GDP, income inequality, average wealth, wealth-income ratio, wealth disparity, and gender inequality are only a few of the indicators cited.

There are now 195 nations on the globe. Currently, the world population will touch 7.9 Billion in April 2022. Each and every country follows a different set of policies which enhances demographic disparities. First, let's look into the national income per adult.

Per Adult National Income:

import plotly
plotly.offline.init_notebook_mode()

import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt

def find_avg(dt):

  lc=dt.shape[1]
  lr=dt.shape[0]
   
  v=[]
  for i in range(2,lc):
    s=np.sum(dt.iloc[:,i])
    d=np.sum(dt.iloc[:,i].isnull())
    v.append(s/(lr-d))

  data=pd.DataFrame(list(zip(dt.columns[2:],v)),columns=["country","value"])

  return data

 


  
def modify(dt):  # to change column names
  a=dt.columns
  lc=dt.shape[1]
  lr=dt.shape[0]
  c=[]
  for i in range(2,lc):
    c.append(a[i].split("\n")[-1])
  dt.columns=["percentile","year"]+c
  return dt
  
  

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-185006.xlsx") # avg anual income _notebooks\data\WID_Data_05042022-185006.xlsx
dt=modify(dt) 
data=find_avg(dt)


# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()



# pie plot
fig = px.pie(data, values='value', names='country')
fig.show()

According to the graph, North America has the greatest per capita income. Hence North America richest country and followed by North America and Oceania. Whereas, East Africa and South Asia have the lowest per capita income.

fig = px.line(dt,x="year",y=dt.columns[2:])
fig.show()

In this graph, we demonstrate how the income of various nations has evolved through time. We looked at the data from 1950 to 2020 and found that North America's economic situation was at the top. Countries in East Africa and Sub-Saharan Africa, for example, have not improved.

Per Adult GDP

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-205222.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()

As we can see from the graph the United Arab Emirates has the highest GDP per adult at 147.9k. Whereas, Burundi with the lowest GDP per adult in the year 2020.

l=[]
 

for i in range(2,dt.shape[1]):
  try:
    fig = px.line(dt,x="year",y=dt.columns[i])
  except:
    l.append(i)
l
k=set(range(2,dt.shape[1]))-set(l)
fig = px.line(dt,x="year",y=dt.columns[list(k)])
fig.show()

D:\anaconda\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`

We've already seen income per adult and GDP per adult. Let's take a look at income disparity.

Income Inequality

Top 1% share.

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-214455.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()

We can see from the plotting that Latin America had the greatest economic inequality.

According to the Suisse Global Wealth Report, the world's richest 1%, individuals with more than $1 million, possess 45.8 percent of the world's wealth.

According to Forbes, the world's top ten billionaires hold a combined fortune of $1.448 trillion, which is more than the entire products and services produced by most countries on an annual basis, according to the World Bank.

l=[]

 

for i in range(2,dt.shape[1]):
  try:
    fig = px.line(dt,x="year",y=dt.columns[i])
  except:
    l.append(i)

l

[14, 98]

k=set(range(2,dt.shape[1]))-set([14,98])
fig = px.line(dt,x="year",y=dt.columns[list(k)])
fig.show()

Top 10% Share:

from IPython.display import HTML

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-215816.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()

Among the 10% share of income inequality, South Africa is at the top.

l=[]
 

for i in range(2,dt.shape[1]):
  try:
    fig = px.line(dt,x="year",y=dt.columns[i])
  except:
    l.append(i)
l
k=set(range(2,dt.shape[1]))-set(l)
fig = px.line(dt,x="year",y=dt.columns[list(k)])
fig.show()

bottom 50 %

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-220500.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()

l=[]
 

for i in range(2,dt.shape[1]):
  try:
    fig = px.line(dt,x="year",y=dt.columns[i])
  except:
    l.append(i)
l
k=set(range(2,dt.shape[1]))-set(l)
fig = px.line(dt,x="year",y=dt.columns[list(k)])
fig.show()

Average wealth.

dt=pd.read_excel("..\_notebooks\data\WID_Data_05042022-220950.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)



# pie plot
fig = px.pie(data, values='value', names='country')
fig.show()

l

[11, 75]

We may deduce a country's average wealth from the pie chart. The ranking was topped by New Zealand and Australia with 18.8%.

fig = px.line(dt,x="year",y=dt.columns[2:])
fig.show()

The plot above depicts a country's average wealth from 1995 to 2020. We can easily see that at the beginning of 1995, North America topped the list, but by 2007, Australia and New Zealand had surpassed North America.

Top 10% share:

dt=pd.read_excel("..\_notebooks\data\WID_Data_06042022-052641.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)


# pie plot
fig = px.pie(data, values='value', names='country')
fig.show()

C:\Users\GIRIYA~1\AppData\Local\Temp/ipykernel_9820/2160476089.py:10: RuntimeWarning:

invalid value encountered in double_scalars

fig = px.scatter(dt,x="year",y=dt.columns[2:])
fig.show()

The examination of a ten percent share of average income from 1995 to 2020. The graph strongly shows a linear fall in the Middle East, and by the year 2015, Latin America had surpassed the Middle East.

bottom 50%

dt=pd.read_excel("..\_notebooks\data\WID_Data_06042022-053817.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=600)
fig.show()

C:\Users\GIRIYA~1\AppData\Local\Temp/ipykernel_9820/2160476089.py:10: RuntimeWarning:

invalid value encountered in double_scalars

fig = px.scatter(dt,x="year",y=dt.columns[2:])
fig.show()

Racial Wealth The United States will be a "majority-minority" nation by the middle of the twenty-first century. Enhancing the economic wellbeing of communities of color will become much more important than it is now if we are to guarantee a healthy middle class, which has historically been the foundation of the national economy. Closing the ongoing "wealth difference" between white and non-white households, which in itself is a societal problem, must now become a primary objective for overall economic policy. Only five African-Americans and 17 Latinos made up less than 5% of the overall population of 15.5 million. CEOs of Fortune global 500 businesses make an average of $1 million each year.

By Contrast, As per the Economic Policy Institute, these demographics combine for 44.1 percent of those in the United States who would benefit from a $15 per hour federal minimum wage by 2025. In the United States, Blacks and Latinos account for 31.9 percent of the total population.

Although this is an overall examination, let us look at the contribution of women to global income.

Female labour income:

dt=pd.read_excel("..\_notebooks\data\WID_Data_06042022-055032.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)

# bar plot
fig = px.bar(data, x='country', y='value',
                color='country', height=400,width=700)
fig.show()

fig = px.line(dt,x="year",y=dt.columns[2:])
fig.show()

This graph depicts the earnings of female workers. Russia and Central Asia are at the top of the graph, accounting for 40% of the country's total income. However, for the Asian nation, it only reached 20%. Through the years the percentage of share of male workers or male work is significantly becoming equal or all men moving towards the average share. But, it is not the case for female workers it is remaining significantly low and constant.

So far, we've looked at inequality from an economic standpoint. However, there are societal factors that are unfavorable to nature such as carbon Emissions. Let us now determine which country has the highest environmental burden.

Corban Emitters

dt=pd.read_excel("..\_notebooks\data\WID_Data_06042022-054644.xlsx") # avg anual income 
dt=modify(dt) 
data=find_avg(dt)


# pie plot
fig = px.pie(data, values='value', names='country')
fig.show()

Although North America is the wealthiest in terms of economics, it is the most damaging to the environment with 42.4% of the world's carbon emissions. Europe ranks second, accounting for 17.3 percent of global carbon emissions.

fig = px.line(dt,x="year",y=dt.columns[2:])
fig.show()

In comparison to the rest of the world, North America emits three times the amount of carbon.

KEY OBSERVATIONS

• Through our analysis, we observe that the income or wealth is mainly accumulated by a very less percentage of the people or countries, or regions.

• Nearly 90% of the total wealth is accumulated with a significantly low percentage(25 %) of the population in each country.

• This depicts that only a few countries are above the average line. And also, when comparing a country basis, we observed that the wealth is associated with only a few developed countries or high resource countries which depicts a high inequality on a regional basis.

• We also observed that analyzing data on a regional basis is more better and efficient as regions depict the resource availability and also strategic importance which we also observed in analyzing.

• With the above statement, we want to say that the regions are having nearly the same shares, like Latin America and the Indian subcontinent.

• Regarding the female income labor share, it is observed that their income share is increasing in parallel to males.

• Coming to the carbon emission, it primarily depicts that more carbon emission implies more industrial productivity which interns depict that this factor also leads to inequality.

Conclusion:

• Gender disparities in educational attainment hinder economic progress both directly and indirectly by decreasing average human capital and influencing investment and population growth. As a result, economic progress decreases fertility and narrows the pay gap between men and women. Lower GDP per capita is linked to a low female-male enrolment ratio. Therefore, if we can increase the literacy rate, we can improve GDP per capita. As a result, one of the few "win-win" development methods might be fostering gender equality in education. It enhances economic prosperity and efficiency, as well as other key human development goals, and it is beneficial in and of itself. Furthermore, industries and cars are highly frequent in industrialized countries. As a result, there is more pollution in the environment. A 1% increase in revenue results in a 0.88 percent increase in emissions.